Methods for Processing or Analyzing a Sample of Thyroid Tissue

1. A method for processing or analyzing a sample of tissue of a subject comprising: (a) obtaining said sample of tissue of said subject, wherein said subject has or is suspected of having a thyroid cancer, and wherein said sample of tissue comprises gene expression products; (b) subjecting a first portion of said sample of tissue to cytological testing that indicates that said first portion of said sample of tissue is indeterminate; (c) upon identifying said first portion of said sample of tissue as indeterminate, assaying by sequencing, array hybridization, or nucleic acid amplification, said gene expression products from a second portion of said sample of tissue, to yield a data set including data corresponding to levels of said gene expression products, wherein said data does not include a plurality of technical factor variables; (d) in a programmed computer, inputting said data including said levels of said gene expression products from (c) to a trained algorithm to generate a classification of said sample of tissue as positive or negative for said thyroid cancer at an accuracy of at least 90%; and (e) electronically outputting a report that identifies said classification of said sample of tissue as positive or negative for said thyroid cancer.

2. The method of claim 1 , wherein said gene expression products include messenger ribonucleic acid (mRNA).

3. The method of claim 2 , wherein said mRNA has a ribonucleic acid integrity number (RIN) of 2.0 or more.

4. The method of claim 2 , wherein a portion of said mRNA is used for multi-gene microarray analysis, and wherein said sample of tissue has an RIN of equal to or less than 5.0.

5. The method of claim 1 , wherein said assaying comprises using one or more of the following: microarray, SAGE, blotting, reverse transcriptase polymerase chain reaction (PCR), or quantitative PCR.

6. The method of claim 1 , wherein said trained algorithm is trained with multiple datasets of levels of gene expression products obtained from a plurality of training samples.

7. The method of claim 1 , wherein said sample of tissue has a benign condition, and wherein said trained algorithm does not classify said sample of tissue as positive for said thyroid cancer.

8. The method of claim 1 , wherein said sample of tissue has a malignant condition, and wherein said trained algorithm classifies said sample of tissue as positive for said thyroid cancer.

9. The method of claim 1 , wherein said trained algorithm generates said classification at an accuracy of at least about 90% for at least two subtypes of said thyroid cancer.

10. The method of claim 9 , wherein said trained algorithm generates said classification at a specificity of at least about 80%.

11. The method of claim 10 , wherein said trained algorithm generates said classification at a specificity of at least about 90%.

12. The method of claim 1 , wherein said trained algorithm generates said classification at a sensitivity of at least about 70%.

13. The method of claim 12 , wherein said trained algorithm generates said classification at a sensitivity of at least about 80%.

14. The method of claim 1 , wherein said trained algorithm is trained for a subset of genes corresponding to said gene expression products.

15. The method of claim 1 , wherein said gene expression products are selected based on a plurality of technical factor variables.

16. The method of claim 1 , wherein said sample of tissue is a sample of thyroid tissue.

17. The method of claim 16 , wherein, (i) when said classification identifies said sample of thyroid tissue as negative for said thyroid cancer, obtaining another sample of thyroid tissue from said subject and repeating (b)-(e) to monitor a change over time in said levels of said gene expression products, or (ii) when said classification identifies said sample of thyroid tissue as positive for said thyroid cancer, treating said subject by a thyroidectomy.

18. The method of claim 1 , wherein said trained algorithm is trained with a training set comprising cytologically indeterminate samples.

19. The method of claim 1 , wherein said trained algorithm is trained with a training set comprising fine needle aspirate (FNA) samples.

20. The method of claim 19 , wherein said training set comprises greater than 300 FNA samples.

21. The method of claim 19 , wherein said training set comprises surgical biopsy samples.

22. The method of claim 1 , wherein said first portion is different from said second portion.

23. The method of claim 1 , further comprising, upon identifying said first portion of said sample of tissue as indeterminate, (i) identifying one or more variants in a third portion of said sample of tissue, and (ii) using said one or more variants and said levels of said gene expression products to generate said classification of said sample of tissue as positive or negative for said thyroid cancer at an accuracy of at least 90%.

24. The method of claim 23 , wherein said third portion is the same as said second portion.

25. The method of claim 1 , wherein said gene expression products comprise micro ribonucleic acid (microRNA).

26. The method of claim 25 , wherein said gene expression products comprise a plurality of microRNAs.

27. The method of claim 1 , wherein (c) comprises processing said gene expression products to generate complementary deoxyribonucleic acid (cDNA) molecules, and sequencing said cDNA molecules to yield said data corresponding to said levels of said gene expression products.

28. The method of claim 1 , further comprising processing said data to remove said plurality of technical factor variables, thereby yielding said data that does not include said plurality of technical factor variables.

29. The method of claim 28 , wherein said plurality of technical factor variables includes one or more members selected from the group consisting of: collection source, collection method, collection media, ribonucleic acid integrity number, whole transcriptome amplification yield, sense strand yield, hybridization site, hybridization quality and experiment batch.